🧭 CompTIA DataSys+ (DS0-001): Master Data Management

Data isn’t a lake. It’s a system. DataSys+ is where you learn to design, govern, and run that system end-to-end—so reports don’t break, pipelines don’t rot, and stakeholders actually trust the numbers.

🎯 Who This Is For

• DBAs and data ops folks stepping into modern pipelines
• BI/analytics teams who must stop firefighting broken refreshes
• GRC/audit pros who need provable lineage, quality, and access control

✅ What You’ll Be Able to Do

• Model data for change: 3NF ↔ star schemas, SCDs, and data contracts
• Engineer reliable ELT/ETL with schedules, retries, and idempotency
• Enforce governance: catalog, lineage, PII controls, retention
• Run operations: SLOs, alerting, incident runbooks, cost hygiene
• Prove quality with tests, reconciliation, and audit-friendly evidence

📚 Core Domains (DataSys+ View)

• Data Design — conceptual/logical/physical, normalization vs dimensional
• Pipelines — batch/stream, orchestration, dependency graphs, backfills
• Storage & Compute — warehouses, lakes, lakehouses; partitioning & performance
• Quality & Security — tests, reconciliation, DQ SLAs, RBAC/ABAC, masking
• Governance & Lifecycle — catalogs, lineage, retention, legal holds
• Operations — monitoring, cost control, capacity, incident mgmt

🏗️ Architecture Patterns that Age Well

• Bronze → Silver → Gold layers with contracts and tests at each hop
• Change Data Capture for low-latency updates without full reloads
• Dimensional marts for BI speed; conformed dimensions to align teams
• Federated access (SSO + groups) with column/row-level security

📆 30-Day Field Plan (60–90 min/day)

Days 1–7 — Design & Contracts

• Model a core domain (orders/customers); write data contracts & SLAs
• Define metrics & grain; pick keys; document slowly changing fields

Days 8–18 — Pipelines & Quality

• Build ELT with orchestration; add retries, backoff, and idempotency
• Add tests: schema, nulls, uniqueness, referential integrity, freshness

Days 19–30 — Governance & Ops

• Catalog + lineage; RBAC; masking for PII; retention policy
• Dashboards for SLOs (latency, freshness, failure rate); write a runbook

💡 Exam Snapshot (DS0-001)

• Mixed items (scenario + practical concepts); proctored online or test center
• Focus: design, implementation, maintenance, security, governance
• Tip: practice with real pipelines and quality checks—don’t cram theory
• Always verify the latest blueprint and policies on CompTIA before booking

🧪 Lab Pack (Hands-On, No Hype)

• Build Bronze→Silver→Gold for a sales dataset with automated tests
• Implement CDC from OLTP to warehouse; validate row counts & deltas
• Set up column/row-level security; mask PII; prove it with queries
• Create a freshness SLO dashboard; alert on missed SLAs
• Backfill scenario: reprocess 90 days safely with idempotent loads

🧾 Portfolio Deliverables (What to Show)

• ERD + star schema diagram with grain and keys
• Pipeline README (sources, transforms, tests, schedules, SLAs)
• DQ report (before/after) + audit-ready evidence pack
• Runbook for incidents: detection → triage → rollback → root cause

📈 Roles You’re Lining Up For

• Data Engineer (junior→mid) / DataOps Engineer
• Database Administrator with modern pipeline ownership
• Analytics Engineer focused on quality and governance

❓ FAQ

Warehouse or lake?
Use both: warehouse for curated, fast analytics; lake for raw + history. Tie them with contracts and lineage.

Which tools?
Any. Principles survive: contracts, tests, lineage, security, and ops discipline.

How do I show impact?
Ship evidence: a green test suite, freshness SLOs, and a dashboard that reconciles to source totals.